In designing an AI workflow which of the following best describes a comprehensive approach to improving the performance of AI agents?

• A. Monitoring agents' throughput and time-to-first-token from the scoring engine
• B. Implementing benchmarking pipelines, deploying physical agents and monitoring user engagement metrics
• C. Implementing benchmarking pipelines and incorporating a dynamic dataset for a real-time fall-back
• D. Implementing benchmarking pipelines, collecting user feedback, and tuning model parameters iteratively

An AI Engineer is analyzing a production agentic AI system's compliance with responsible AI standards.
Which evaluation approaches effectively identify potential safety vulnerabilities and ethical risks in multi- agent workflows? (Choose two.)

• A. Emphasize latency metrics and throughput performance as key evaluation factors for safety vulnerabilities, providing a baseline for operational measures and resource allocation.
• B. Use user feedback as a primary signal for risk identification, emphasizing post-deployment observations and qualitative experience reports alongside operational monitoring.
• C. Deploy multi-layered evaluation combining bias detection metrics (demographic parity, equalized odds) with adversarial testing to probe agent responses for harmful outputs across diverse user populations
• D. Implement comprehensive audit trails using NVIDIA NeMo Guardrails with semantic similarity checks, tracking agent decisions across conversation flows and evaluating policy violations through automated compliance scoring.

Your support agent frequently fails to complete tasks when third-party tools return unexpected formats.
Which solution improves resilience against these failures?

• A. Reduce the number of tools available to avoid bad integrations
• B. Use deterministic temperature settings for all generations
• C. Add robust schema validation and exception handling for all tool outputs
• D. Re-train the model to avoid the use of third-party tools entirely

A technology startup is preparing to launch an AI agent platform to serve clients with unpredictable usage patterns. They face periods of high user activity and low demand, so their deployment approach must minimize wasted resources during slow times and automatically allocate more resources during busy periods
- all while keeping operational costs reasonable.
Given these requirements, which deployment strategy most effectively ensures both cost-effectiveness and adaptability for scaling agentic AI systems?

• A. Monitoring system logs for usage patterns and making infrastructure changes after monthly analysis
• B. Using fixed-size virtual machine clusters to guarantee consistent resource allocation at all times
• C. Scheduling periodic manual reviews to increase or decrease infrastructure based on predicted user numbers
• D. Implementing autoscaling policies in a container orchestration environment to automatically adjust resources according to workload changes